Stress x EE MA
01/07/2021
Loading packages
knitr::opts_chunk$set(echo = TRUE)
pacman::p_load(tidyverse,
here,
metafor,
emmeans,
orchaRd)Loading data and functions
dat <- read_csv(here("Data","Full_data.csv"))
# Load custom function to extract data
source(here("R/functions.R")) Data organisation
Getting effect sizes from function, ‘flipping’ effect sizes so that all effect sizes are higher values = individuals do better and learning/memory, and shifting negative values to possitive as lnRR cannot use negative values
#fixing negative values in study 16 (this doesn't work as can't use 0 in lnRR calculation)
#modifying study 16 that has negative values: shifting everything up by the lowest value
#note, this results in inf lnRR - is it because of the zeros?
#dat1 <- dat %>%
# rowwise() %>%
#mutate(min.mean = min(CC_mean, EC_mean, CS_mean, ES_mean)) %>%
#ungroup() %>%
#mutate(CC_mean = case_when(First_author == "Wang" ~ CC_mean + abs(min.mean),
# TRUE ~ as.numeric(.$CC_mean))) %>%
#mutate(EC_mean = case_when(First_author == "Wang" ~ EC_mean + abs(min.mean),
#TRUE ~ as.numeric(.$EC_mean))) %>%
#mutate(CS_mean = case_when(First_author == "Wang" ~ CS_mean + abs(min.mean),
#TRUE ~ as.numeric(.$CS_mean))) %>%
#mutate(ES_mean = case_when(First_author == "Wang" ~ ES_mean + abs(min.mean),
#TRUE ~ as.numeric(.$ES_mean)))
#Getting effect sizes
effect_size <- effect_set(CC_n = "CC_n", CC_mean = "CC_mean", CC_SD = "CC_SD",
EC_n = "EC_n", EC_mean = "EC_mean" , EC_SD ="EC_SD",
CS_n = "CS_n", CS_mean = "CS_mean", CS_SD = "CS_SD",
ES_n = "ES_n", ES_mean = "ES_mean", ES_SD = "ES_SD",
data = dat)
#Removing missing effect sizes
full_info <- which(complete.cases(effect_size) == TRUE)
dat_effect <- cbind(dat, effect_size)
dat <- dat_effect[full_info, ]
#Flipping 'lower is better' effect sizes
#flipping lnRR for values where higher = worse
dat$lnRR_Ea <- ifelse(dat$Response_direction == 2, dat$lnRR_E*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_E)) # currently NAswhich causes error
dat$lnRR_Sa <- ifelse(dat$Response_direction == 2, dat$lnRR_S*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_S)) # currently NAswhich causes error
dat$lnRR_ESa <- ifelse(dat$Response_direction == 2, dat$lnRR_ES*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$lnRR_ES)) # currently NAswhich causes error
#flipping SMD
dat$SMD_Ea <- ifelse(dat$Response_direction == 2, dat$SMD_E*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_E)) # currently NAswhich causes error
dat$SMD_Sa <- ifelse(dat$Response_direction == 2, dat$SMD_S*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_S)) # currently NAswhich causes error
dat$SMD_ESa <- ifelse(dat$Response_direction == 2, dat$SMD_ES*-1,ifelse(is.na(dat$Response_direction) == TRUE, NA, dat$SMD_ES))Assigning human readable words to moderators
dat <- dat %>% mutate(Type_learning = case_when(Type_learning == 1 ~ "Habituation",
Type_learning == 2 ~ "Conditioning",
Type_learning == 3 ~ "Recognition",
Type_learning == 4 ~ "Unclear"),
Learning_vs_memory = case_when(Learning_vs_memory == 1 ~ "Learning",
Learning_vs_memory == 2 ~ "Memory",
Learning_vs_memory == 1 ~ "Unclear"),
Appetitive_vs_aversive = case_when(Appetitive_vs_aversive == 1 ~"Appetitive",
Appetitive_vs_aversive == 2 ~ "Aversive",
Appetitive_vs_aversive == 3 ~ "Not applicable",
Appetitive_vs_aversive == 4 ~ "Unclear"),
Type_stress_exposure = case_when(Type_stress_exposure == 1 ~ "Density",
Type_stress_exposure == 2 ~ "Scent",
Type_stress_exposure == 3 ~ "Shock",
Type_stress_exposure == 4 ~ "Exertion",
Type_stress_exposure == 5 ~ "Restraint",
Type_stress_exposure == 6 ~ "MS",
Type_stress_exposure == 7 ~ "Circadian rhythm",
Type_stress_exposure == 8 ~ "Noise",
Type_stress_exposure == 9 ~ "Other",
Type_stress_exposure == 10 ~ "Combination",
Type_stress_exposure == 11 ~ "unclear"),
Age_stress_exposure = case_when(Age_stress_exposure == 1 ~ "Prenatal",
Age_stress_exposure == 2 ~ "Juvenile",
Age_stress_exposure == 3 ~ "Adult",
Age_stress_exposure == 4 ~ "Unclear"),
Stress_duration = case_when(Stress_duration == 1 ~ "Acute",
Stress_duration == 2 ~ "Chronic",
Stress_duration == 3 ~ "Intermittent",
Stress_duration == 4 ~ "Unclear"),
EE_social = case_when(EE_social == 1 ~ "Social",
EE_social== 2 ~ "Non-social",
EE_social == 3 ~ "Unclear"),
EE_exercise = case_when(EE_exercise == 1 ~ "Exercise",
EE_exercise == 2 ~ "No exercise"),
Age_EE_exposure = case_when(Age_EE_exposure == 1 ~ "Prenatal",
Age_EE_exposure == 2 ~ "Juvenile",
Age_EE_exposure == 3 ~ "Adult",
Age_EE_exposure == 4 ~ "Unclear"))Modelling with lnRR
Things that remain to be done: - Incorporate strain as random effect - Consider including VCV - Check outlier
Stress
Intercept model
Learning and memory are significantly reduced due to stress. High heterogeneity
mod_S0 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S0) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -7.9957 15.9913 21.9913 29.5239 22.2672
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0182 0.1350 30 no Study_ID
## sigma^2.2 0.0254 0.1592 92 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 91) = 710.8944, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## -0.0905 0.0341 -2.6531 91 0.0094 -0.1582 -0.0227 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_S0) ## I2_total I2_Study_ID I2_ES_ID
## 0.9282014 0.3882766 0.5399248funnel(mod_S0)
orchard_plot(mod_S0, mod = "Int", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression
Type of learning
The type of learning/memory response
dat$Type_learning<-as.factor(dat$Type_learning)
mod_S1 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Type_learning-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S1)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## 1.4516 -2.9033 9.0967 23.9608 10.1338
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0294 0.1715 30 no Study_ID
## sigma^2.2 0.0072 0.0847 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 561.5739, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 12.3231, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_learningConditioning -0.1021 0.0384 -2.6609 88 0.0093 -0.1783
## Type_learningHabituation -0.2833 0.0750 -3.7770 88 0.0003 -0.4323
## Type_learningRecognition -0.0059 0.0522 -0.1137 88 0.9098 -0.1097
## Type_learningUnclear 0.5023 0.1210 4.1496 88 <.0001 0.2617
## ci.ub
## Type_learningConditioning -0.0258 **
## Type_learningHabituation -0.1342 ***
## Type_learningRecognition 0.0978
## Type_learningUnclear 0.7428 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S1) ## R2_marginal R2_coditional
## 0.1777244 0.8386421# Orchard plot
orchard_plot(mod_S1, mod = "Type_learning", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_S2 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID, ~1|ES_ID),
test = "t",
data = dat)
summary(mod_S2) ##
## Multivariate Meta-Analysis Model (k = 85; method: REML)
##
## logLik Deviance AIC BIC AICc
## -6.2296 12.4591 20.4591 30.1345 20.9720
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0294 0.1715 30 no Study_ID
## sigma^2.2 0.0142 0.1192 85 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 83) = 620.2415, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 83) = 2.9245, p-val = 0.0592
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning -0.0305 0.0484 -0.6304 83 0.5302 -0.1269
## Learning_vs_memoryMemory -0.0918 0.0399 -2.3027 83 0.0238 -0.1712
## ci.ub
## Learning_vs_memoryLearning 0.0658
## Learning_vs_memoryMemory -0.0125 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S2) ## R2_marginal R2_coditional
## 0.0188891 0.6804689# Orchard plot
orchard_plot(mod_S2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Appetitive vs aversive
The type of cue used
mod_S3 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~ Appetitive_vs_aversive-1, random = list(~1|Study_ID, ~1|ES_ID),
test = "t",
data = dat)
summary(mod_S3)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -1.5385 3.0770 15.0770 29.9410 16.1140
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0235 0.1533 30 no Study_ID
## sigma^2.2 0.0126 0.1124 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 495.4829, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 7.2763, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Appetitive_vs_aversiveAppetitive -0.1883 0.0778 -2.4200 88 0.0176
## Appetitive_vs_aversiveAversive -0.0980 0.0414 -2.3685 88 0.0201
## Appetitive_vs_aversiveNot applicable -0.0357 0.0488 -0.7325 88 0.4658
## Appetitive_vs_aversiveUnclear 0.5427 0.1435 3.7809 88 0.0003
## ci.lb ci.ub
## Appetitive_vs_aversiveAppetitive -0.3429 -0.0337 *
## Appetitive_vs_aversiveAversive -0.1802 -0.0158 *
## Appetitive_vs_aversiveNot applicable -0.1326 0.0612
## Appetitive_vs_aversiveUnclear 0.2574 0.8279 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S3) ## R2_marginal R2_coditional
## 0.1630982 0.7076434# Orchard plot
orchard_plot(mod_S3, mod = "Appetitive_vs_aversive", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Type of stress
The type of stress manipulation
mod_S4 <- rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Type_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S4) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -7.6635 15.3270 33.3270 55.3109 35.7270
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0199 0.1410 30 no Study_ID
## sigma^2.2 0.0265 0.1627 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 85) = 592.3192, p-val < .0001
##
## Test of Moderators (coefficients 1:7):
## F(df1 = 7, df2 = 85) = 1.5074, p-val = 0.1757
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_stress_exposureCombination -0.0688 0.0943 -0.7300 85 0.4674 -0.2562
## Type_stress_exposureDensity 0.1633 0.2685 0.6084 85 0.5446 -0.3705
## Type_stress_exposureMS -0.0516 0.0544 -0.9484 85 0.3456 -0.1597
## Type_stress_exposureNoise -0.0674 0.0992 -0.6789 85 0.4990 -0.2647
## Type_stress_exposureOther -0.1817 0.2552 -0.7121 85 0.4784 -0.6892
## Type_stress_exposureRestraint -0.2017 0.0734 -2.7488 85 0.0073 -0.3475
## Type_stress_exposureShock -0.0746 0.1570 -0.4754 85 0.6357 -0.3868
## ci.ub
## Type_stress_exposureCombination 0.1186
## Type_stress_exposureDensity 0.6972
## Type_stress_exposureMS 0.0565
## Type_stress_exposureNoise 0.1299
## Type_stress_exposureOther 0.3257
## Type_stress_exposureRestraint -0.0558 **
## Type_stress_exposureShock 0.2375
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S4)## R2_marginal R2_coditional
## 0.08052616 0.47495458# Orchard plot
orchard_plot(mod_S4, mod = "Type_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Age of stress
The age at which the individuals were exposed to the stressor. Note there are a lot of ‘unkown’ age as authors only report PND which needs to be researched. I’m wondering if this also needs an ‘adolescence’ category as this seesm to be popular in rodent research
mod_S5 <-rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Age_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S5) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -6.7020 13.4041 25.4041 40.2681 26.4411
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0152 0.1235 30 no Study_ID
## sigma^2.2 0.0263 0.1621 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 573.2107, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 3.1056, p-val = 0.0193
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Age_stress_exposureAdult -0.1557 0.0805 -1.9332 88 0.0564 -0.3158
## Age_stress_exposureJuvenile -0.0293 0.0436 -0.6722 88 0.5032 -0.1159
## Age_stress_exposurePrenatal -0.1679 0.0911 -1.8425 88 0.0688 -0.3490
## Age_stress_exposureUnclear -0.1888 0.0858 -2.1996 88 0.0305 -0.3593
## ci.ub
## Age_stress_exposureAdult 0.0044 .
## Age_stress_exposureJuvenile 0.0573
## Age_stress_exposurePrenatal 0.0132 .
## Age_stress_exposureUnclear -0.0182 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S5) ## R2_marginal R2_coditional
## 0.1010016 0.4310582# Orchard plot
orchard_plot(mod_S5, mod = "Age_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Acute vs chronic stress
How long was the stress applied for (chronic = every day for 7 days or more)? This has the highest marginal R2
mod_S6 <-rma.mv(yi = lnRR_Sa, V = lnRRV_S, mod = ~Stress_duration-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_S6) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -6.2217 12.4434 24.4434 39.3074 25.4804
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0154 0.1239 30 no Study_ID
## sigma^2.2 0.0259 0.1609 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 676.5571, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 3.4245, p-val = 0.0119
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Stress_durationAcute 0.0674 0.0759 0.8881 88 0.3769 -0.0835
## Stress_durationChronic -0.1242 0.0383 -3.2470 88 0.0017 -0.2003
## Stress_durationIntermittent -0.2406 0.1653 -1.4550 88 0.1492 -0.5692
## Stress_durationUnclear -0.0742 0.1486 -0.4995 88 0.6187 -0.3696
## ci.ub
## Stress_durationAcute 0.2184
## Stress_durationChronic -0.0482 **
## Stress_durationIntermittent 0.0880
## Stress_durationUnclear 0.2211
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_S6) ## R2_marginal R2_coditional
## 0.1415118 0.4612428# Orchard plot
orchard_plot(mod_S6, mod = "Stress_duration", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Environmental enrichment
Intercept model
Learning and memory are significantly improved when housed with environmnetal enrichment
mod_E0 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E0) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -11.7779 23.5558 29.5558 37.0883 29.8316
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0109 0.1045 30 no Study_ID
## sigma^2.2 0.0348 0.1865 92 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 91) = 852.3549, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.2016 0.0319 6.3148 91 <.0001 0.1382 0.2650 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_E0) ## I2_total I2_Study_ID I2_ES_ID
## 0.9313106 0.2224231 0.7088875funnel(mod_E0)
#trying orchard plot
orchard_plot(mod_E0, mod = "Int", xlab = "lnRR", alpha=0.4) + # Orchard plot
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5)+ # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2)+ # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_colour_manual(values = "darkorange")+ # change colours
scale_fill_manual(values="darkorange")+
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression
Type of learning
The type of learning/memory response
mod_E1 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~Type_learning-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E1)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -9.0089 18.0178 30.0178 44.8818 31.0548
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0202 0.1423 30 no Study_ID
## sigma^2.2 0.0262 0.1619 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 797.6615, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 10.7294, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_learningConditioning 0.2474 0.0392 6.3159 88 <.0001 0.1696
## Type_learningHabituation 0.1971 0.0945 2.0855 88 0.0399 0.0093
## Type_learningRecognition 0.0622 0.0639 0.9734 88 0.3330 -0.0648
## Type_learningUnclear -0.1895 0.1929 -0.9824 88 0.3286 -0.5729
## ci.ub
## Type_learningConditioning 0.3253 ***
## Type_learningHabituation 0.3850 *
## Type_learningRecognition 0.1891
## Type_learningUnclear 0.1939
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E1) ## R2_marginal R2_coditional
## 0.1178423 0.5021285# Orchard plot
orchard_plot(mod_E1, mod = "Type_learning", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_E2 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E2) ##
## Multivariate Meta-Analysis Model (k = 85; method: REML)
##
## logLik Deviance AIC BIC AICc
## -5.9388 11.8775 19.8775 29.5529 20.3904
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0163 0.1278 30 no Study_ID
## sigma^2.2 0.0197 0.1403 85 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 83) = 623.3556, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 83) = 18.5216, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning 0.2033 0.0460 4.4161 83 <.0001 0.1117
## Learning_vs_memoryMemory 0.1951 0.0354 5.5142 83 <.0001 0.1247
## ci.ub
## Learning_vs_memoryLearning 0.2949 ***
## Learning_vs_memoryMemory 0.2654 ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E2) ## R2_marginal R2_coditional
## 0.0004189897 0.4536360943# Orchard plot
orchard_plot(mod_E2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Appetitive vs aversive
The type of cue used
mod_E3 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~ Appetitive_vs_aversive-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E3)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -6.6058 13.2116 25.2116 40.0756 26.2486
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0207 0.1438 30 no Study_ID
## sigma^2.2 0.0232 0.1525 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 537.8318, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 12.1954, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval
## Appetitive_vs_aversiveAppetitive 0.2204 0.0794 2.7750 88 0.0067
## Appetitive_vs_aversiveAversive 0.2695 0.0434 6.2018 88 <.0001
## Appetitive_vs_aversiveNot applicable 0.0679 0.0536 1.2658 88 0.2089
## Appetitive_vs_aversiveUnclear -0.1779 0.1821 -0.9769 88 0.3313
## ci.lb ci.ub
## Appetitive_vs_aversiveAppetitive 0.0626 0.3782 **
## Appetitive_vs_aversiveAversive 0.1831 0.3558 ***
## Appetitive_vs_aversiveNot applicable -0.0387 0.1745
## Appetitive_vs_aversiveUnclear -0.5399 0.1840
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E3) ## R2_marginal R2_coditional
## 0.1626053 0.5568014# Orchard plot
orchard_plot(mod_E3, mod = "Appetitive_vs_aversive", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Exercise enrichment
Does the form of enrichment include exercise through a running wheel or treadmill?
mod_E5<- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~EE_exercise-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E5)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -12.0257 24.0514 32.0514 42.0507 32.5220
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0120 0.1097 30 no Study_ID
## sigma^2.2 0.0349 0.1868 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 90) = 811.3918, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 90) = 19.1476, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_exerciseExercise 0.2020 0.0401 5.0382 90 <.0001 0.1224 0.2817
## EE_exerciseNo exercise 0.2011 0.0560 3.5932 90 0.0005 0.0899 0.3123
##
## EE_exerciseExercise ***
## EE_exerciseNo exercise ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E5) ## R2_marginal R2_coditional
## 4.268180e-06 2.562894e-01# Orchard plot
orchard_plot(mod_E5, mod = "EE_exercise", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Age of enrichment
The age at which the individuals were exposed to environmental enrichment.
mod_E6 <- rma.mv(yi = lnRR_Ea, V = lnRRV_E, mod = ~Age_EE_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_E6) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -10.0138 20.0276 30.0276 42.4708 30.7505
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0090 0.0951 30 no Study_ID
## sigma^2.2 0.0355 0.1884 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 797.4346, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 15.7420, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Age_EE_exposureAdult 0.1859 0.0614 3.0259 89 0.0032 0.0638 0.3080
## Age_EE_exposureJuvenile 0.0033 0.1008 0.0331 89 0.9736 -0.1970 0.2037
## Age_EE_exposureUnclear 0.2362 0.0383 6.1700 89 <.0001 0.1601 0.3122
##
## Age_EE_exposureAdult **
## Age_EE_exposureJuvenile
## Age_EE_exposureUnclear ***
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_E6) ## R2_marginal R2_coditional
## 0.09611387 0.27963158# Orchard plot
orchard_plot(mod_E6, mod = "Age_EE_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Interaction of stress and EE
Intercept
Enriched and stress animals are better at learning and memory. TODO: It looks like there is a large but low precision outlier. Should potentially remove?
mod_ES0 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES0) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -43.9321 87.8643 93.8643 101.3968 94.1401
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0525 0.2292 30 no Study_ID
## sigma^2.2 0.0162 0.1274 92 no ES_ID
##
## Test for Heterogeneity:
## Q(df = 91) = 314.3092, p-val < .0001
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## 0.1494 0.0515 2.8997 91 0.0047 0.0470 0.2517 **
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1i2_ml(mod_ES0) ## I2_total I2_Study_ID I2_ES_ID
## 0.8360427 0.6387796 0.1972631funnel(mod_ES0)
orchard_plot(mod_ES0, mod = "Int", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Meta-regression
Type of learning
The type of learning/memory response
mod_ES1 <- rma.mv(yi = lnRR_ESa, V = lnRRV_E, mod = ~Type_learning-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES1)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -62.8990 125.7980 137.7980 152.6620 138.8350
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0873 0.2955 30 no Study_ID
## sigma^2.2 0.0912 0.3021 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 1253.0271, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 3.3740, p-val = 0.0129
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_learningConditioning 0.2424 0.0723 3.3549 88 0.0012 0.0988
## Type_learningHabituation 0.2281 0.1662 1.3724 88 0.1734 -0.1022
## Type_learningRecognition -0.0450 0.1158 -0.3890 88 0.6982 -0.2752
## Type_learningUnclear 0.0003 0.3577 0.0009 88 0.9993 -0.7106
## ci.ub
## Type_learningConditioning 0.3861 **
## Type_learningHabituation 0.5583
## Type_learningRecognition 0.1851
## Type_learningUnclear 0.7113
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES1) ## R2_marginal R2_coditional
## 0.05851901 0.51888047# Orchard plot
orchard_plot(mod_ES1, mod = "Type_learning", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Learning vs Memory
Is the assay broadly measuring learning or memory?
mod_ES2 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Learning_vs_memory-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES2) ##
## Multivariate Meta-Analysis Model (k = 85; method: REML)
##
## logLik Deviance AIC BIC AICc
## -45.5256 91.0513 99.0513 108.7266 99.5641
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0518 0.2277 30 no Study_ID
## sigma^2.2 0.0192 0.1386 85 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 83) = 306.7981, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 83) = 4.8150, p-val = 0.0105
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Learning_vs_memoryLearning 0.2064 0.0691 2.9855 83 0.0037 0.0689
## Learning_vs_memoryMemory 0.1266 0.0553 2.2887 83 0.0246 0.0166
## ci.ub
## Learning_vs_memoryLearning 0.3439 **
## Learning_vs_memoryMemory 0.2366 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES2) ## R2_marginal R2_coditional
## 0.0196254 0.7348983# Orchard plot
orchard_plot(mod_ES2, mod = "Learning_vs_memory", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Appetitive vs aversive
The type of cue used
mod_ES3 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~ Appetitive_vs_aversive-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES3)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.4312 80.8624 92.8624 107.7264 93.8994
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0516 0.2272 30 no Study_ID
## sigma^2.2 0.0100 0.0999 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 276.6164, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 4.6425, p-val = 0.0019
##
## Model Results:
##
## estimate se tval df pval
## Appetitive_vs_aversiveAppetitive 0.2225 0.1173 1.8973 88 0.0611
## Appetitive_vs_aversiveAversive 0.2102 0.0607 3.4619 88 0.0008
## Appetitive_vs_aversiveNot applicable 0.0052 0.0692 0.0750 88 0.9404
## Appetitive_vs_aversiveUnclear -0.0009 0.1621 -0.0054 88 0.9957
## ci.lb ci.ub
## Appetitive_vs_aversiveAppetitive -0.0106 0.4556 .
## Appetitive_vs_aversiveAversive 0.0895 0.3308 ***
## Appetitive_vs_aversiveNot applicable -0.1323 0.1426
## Appetitive_vs_aversiveUnclear -0.3229 0.3212
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES3) ## R2_marginal R2_coditional
## 0.1183089 0.8572186# Orchard plot
orchard_plot(mod_ES3, mod = "Appetitive_vs_aversive", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Type of stress
The type of stress manipulation
mod_ES4 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Type_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES4)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -40.6944 81.3887 99.3887 121.3726 101.7887
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0657 0.2564 30 no Study_ID
## sigma^2.2 0.0167 0.1291 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 85) = 279.7273, p-val < .0001
##
## Test of Moderators (coefficients 1:7):
## F(df1 = 7, df2 = 85) = 1.6555, p-val = 0.1311
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Type_stress_exposureCombination 0.0768 0.1474 0.5212 85 0.6036 -0.2162
## Type_stress_exposureDensity -0.5500 0.4305 -1.2777 85 0.2048 -1.4059
## Type_stress_exposureMS 0.1669 0.0879 1.9000 85 0.0608 -0.0078
## Type_stress_exposureNoise 0.1804 0.1638 1.1016 85 0.2738 -0.1452
## Type_stress_exposureOther 0.6707 0.4141 1.6196 85 0.1090 -0.1527
## Type_stress_exposureRestraint 0.1535 0.1163 1.3199 85 0.1904 -0.0777
## Type_stress_exposureShock 0.1527 0.2169 0.7041 85 0.4833 -0.2785
## ci.ub
## Type_stress_exposureCombination 0.3699
## Type_stress_exposureDensity 0.3059
## Type_stress_exposureMS 0.3416 .
## Type_stress_exposureNoise 0.5061
## Type_stress_exposureOther 1.4942
## Type_stress_exposureRestraint 0.3848
## Type_stress_exposureShock 0.5838
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES4)## R2_marginal R2_coditional
## 0.1283499 0.8237461# Orchard plot
orchard_plot(mod_ES4, mod = "Type_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Age of stress
The age at which the individuals were exposed to the stressor. Note there are a lot of ‘unkown’ age as authors only report PND which needs to be researched. I’m wondering if this also needs an ‘adolescence’ category as this seesm to be popular in rodent research
mod_ES5 <-rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Age_stress_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES5) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -41.1116 82.2231 94.2231 109.0872 95.2602
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0470 0.2167 30 no Study_ID
## sigma^2.2 0.0154 0.1243 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 258.1063, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 3.6152, p-val = 0.0089
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Age_stress_exposureAdult 0.0523 0.1160 0.4508 88 0.6532 -0.1782
## Age_stress_exposureJuvenile 0.0920 0.0669 1.3762 88 0.1722 -0.0409
## Age_stress_exposurePrenatal 0.3768 0.1385 2.7217 88 0.0078 0.1017
## Age_stress_exposureUnclear 0.2876 0.1292 2.2262 88 0.0286 0.0309
## ci.ub
## Age_stress_exposureAdult 0.2827
## Age_stress_exposureJuvenile 0.2249
## Age_stress_exposurePrenatal 0.6520 **
## Age_stress_exposureUnclear 0.5443 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES5) ## R2_marginal R2_coditional
## 0.1526494 0.7902692# Orchard plot
orchard_plot(mod_ES5, mod = "Age_stress_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Acute vs chronic stress
How long was the stress applied for (chronic = every day for 7 days or more)? This has the highest marginal R2 (currentl nearly 43%) - need to redo without outlier
mod_ES6 <-rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Stress_duration-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES6) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -37.7221 75.4441 87.4441 102.3081 88.4812
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0319 0.1787 30 no Study_ID
## sigma^2.2 0.0172 0.1310 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 88) = 278.3176, p-val < .0001
##
## Test of Moderators (coefficients 1:4):
## F(df1 = 4, df2 = 88) = 6.6034, p-val = 0.0001
##
## Model Results:
##
## estimate se tval df pval ci.lb
## Stress_durationAcute -0.1490 0.1053 -1.4144 88 0.1608 -0.3583
## Stress_durationChronic 0.1967 0.0514 3.8297 88 0.0002 0.0946
## Stress_durationIntermittent 0.6636 0.2209 3.0042 88 0.0035 0.2246
## Stress_durationUnclear 0.1479 0.1741 0.8492 88 0.3980 -0.1982
## ci.ub
## Stress_durationAcute 0.0603
## Stress_durationChronic 0.2987 ***
## Stress_durationIntermittent 1.1025 **
## Stress_durationUnclear 0.4940
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES6) ## R2_marginal R2_coditional
## 0.3702872 0.7799714# Orchard plot
orchard_plot(mod_ES6, mod = "Stress_duration", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Social enrichment
Does EE also include a manipulation of social environment (i.e., number of individuals in EE relative to control)?
mod_ES7<- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~EE_social-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES7)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.6997 85.3994 95.3994 107.8426 96.1223
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0521 0.2282 30 no Study_ID
## sigma^2.2 0.0166 0.1289 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 309.9047, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 3.4909, p-val = 0.0189
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_socialNon-social 0.1171 0.0798 1.4663 89 0.1461 -0.0416 0.2757
## EE_socialSocial 0.1937 0.0691 2.8039 89 0.0062 0.0564 0.3310 **
## EE_socialUnclear -0.1958 0.2883 -0.6789 89 0.4990 -0.7687 0.3772
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES7) ## R2_marginal R2_coditional
## 0.0571462 0.7718836# Orchard plot
orchard_plot(mod_ES7, mod = "EE_social", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Exercise enrichment
Does the form of enrichment include exercise through a running wheel or treadmill?
mod_ES8<- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~EE_exercise-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES8)##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -43.5328 87.0656 95.0656 105.0649 95.5362
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0528 0.2299 30 no Study_ID
## sigma^2.2 0.0165 0.1283 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 90) = 277.3317, p-val < .0001
##
## Test of Moderators (coefficients 1:2):
## F(df1 = 2, df2 = 90) = 4.3500, p-val = 0.0157
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## EE_exerciseExercise 0.1290 0.0628 2.0549 90 0.0428 0.0043 0.2536 *
## EE_exerciseNo exercise 0.1924 0.0909 2.1159 90 0.0371 0.0118 0.3730 *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES8) ## R2_marginal R2_coditional
## 0.01312899 0.76550033# Orchard plot
orchard_plot(mod_ES8, mod = "EE_exercise", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Age of enrichment
The age at which the individuals were exposed to environmental enrichment.
mod_ES9 <- rma.mv(yi = lnRR_ESa, V = lnRRV_ES, mod = ~Age_EE_exposure-1, random = list(~1|Study_ID,
~1|ES_ID),
test = "t",
data = dat)
summary(mod_ES9) ##
## Multivariate Meta-Analysis Model (k = 92; method: REML)
##
## logLik Deviance AIC BIC AICc
## -42.9629 85.9259 95.9259 108.3691 96.6488
##
## Variance Components:
##
## estim sqrt nlvls fixed factor
## sigma^2.1 0.0547 0.2340 30 no Study_ID
## sigma^2.2 0.0170 0.1305 92 no ES_ID
##
## Test for Residual Heterogeneity:
## QE(df = 89) = 299.7681, p-val < .0001
##
## Test of Moderators (coefficients 1:3):
## F(df1 = 3, df2 = 89) = 3.0451, p-val = 0.0329
##
## Model Results:
##
## estimate se tval df pval ci.lb ci.ub
## Age_EE_exposureAdult 0.1576 0.1030 1.5299 89 0.1296 -0.0471 0.3623
## Age_EE_exposureJuvenile -0.0146 0.1733 -0.0840 89 0.9332 -0.3589 0.3298
## Age_EE_exposureUnclear 0.1693 0.0650 2.6053 89 0.0108 0.0402 0.2985
##
## Age_EE_exposureAdult
## Age_EE_exposureJuvenile
## Age_EE_exposureUnclear *
##
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1r2_ml(mod_ES9) ## R2_marginal R2_coditional
## 0.03931516 0.77207337# Orchard plot
orchard_plot(mod_ES9, mod = "Age_EE_exposure", xlab = "lnRR", alpha=0.4) +
geom_errorbarh(aes(xmin = lowerPR, xmax = upperPR), height = 0, show.legend = FALSE, size = 1.1, alpha = 0.5) + # prediction intervals
geom_errorbarh(aes(xmin = lowerCL, xmax = upperCL), height = 0.05, show.legend = FALSE, size = 2) + # confidence intervals
geom_point(aes(fill = name), size = 5, shape = 21)+ # mean estimate
scale_size_continuous(range = c(1, 7))+ # change point scaling
theme(panel.border = element_rect(colour = "black", fill=NA, size=1.3), # border around the plot
text = element_text(size = 24), # change font sizes
legend.title = element_text(size = 15),
legend.text = element_text(size = 13)) 
Social enrichment
Does EE also include a manipulation of social environment? Note that we excluded any studies that exclusively used social enrichment.s